12 Working with your stakeholders

AI products magnify the complexity of stakeholder communication, demanding that product leaders act as educators, translators, and advocates who align diverse perspectives around business outcomes and user value. Framed through Mark’s development of a predictive analytics platform for supply chain management, the chapter shows how uncertainty, interdisciplinary work, and human–AI interaction raise the bar for clarity, trust-building, and expectation management. It lays out a practical roadmap for navigating three arenas—collaboration within the AI team, communication with internal business stakeholders, and engagement with customers and users—while emphasizing user-centered goals, iterative learning, and transparency as the foundation for adoption and impact.

Inside the product team, effective collaboration hinges on deeply understanding roles (data science, engineering, UX, and domain expertise), converging on user-focused metrics, and embracing experimentation. Tactics include adopting tightly knit working models (e.g., a product trio), making problem framing and trade-offs explicit, and translating across different “languages” of success (accuracy, latency, usability, domain fidelity). The chapter then extends to business stakeholders: for executives, lead with outcomes and a simple case for ROI and next steps; for sales and marketing, equip clear narratives and differentiation without overpromising; for customer success, combine scenario-based guidance with transparency about limitations; and for legal/compliance, proactively address privacy, bias, data handling, and auditability. Across all groups, precise, jargon-free communication that anticipates concerns sustains momentum and trust.

Externally, the chapter focuses on communicating value in concrete terms—efficiency and productivity gains, improved decisions, personalization, innovation potential, convenience, and even emotional benefits—while being explicit about AI’s failure modes, error frequency, and safeguards. It recommends giving users tools to spot, override, and report errors; building channels for proactive updates; addressing core anxieties (trust, job replacement, privacy) with empathy and control; and educating users to interpret probabilistic outputs and collaborate with the system. Finally, it advocates turning users into co-creators through continuous feedback loops and recognition, and tailoring strategies to B2B versus B2C realities—different decision-makers, onboarding depth, channels, and adoption paths. The common thread is disciplined transparency, evidence-backed value, and two-way learning that compounds product quality and stakeholder confidence over time.

The major stakeholders of an AI-driven product

• Learn the responsibilities and priorities of your team members—whether they are engineers, data scientists, or UX designers—to reduce friction and enable effective collaboration.
• Shift focus from individual role-specific metrics (e.g., model accuracy or latency) to shared outcomes like user satisfaction and adoption rates to ensure the product delivers meaningful value.
• Acknowledge that AI involves experimentation and learning. Iterate collaboratively, treating insights and methods developed during the process as valuable intellectual property.
• Tailor messaging to business stakeholders by focusing on outcomes like ROI, operational efficiency, and market positioning rather than technical details.
• For B2B, highlight strategic benefits (e.g., cost reduction, productivity gains) to decision-makers, and practical improvements (e.g., simplifying daily workflows) for end users. In B2C, focus on personalized, intuitive, and emotionally engaging experiences.
• Tackle fears of job displacement by positioning AI as a tool that enhances rather than replaces human expertise, and offer a forward-looking perspective on how roles can evolve.
• Build trust by openly explaining how the AI works, its limitations, and what users can expect. Use confidence intervals, explainable models, and real-world examples to make the system more approachable.
• Be upfront about the types and frequency of errors, their potential impact, and how users can identify and manage them. Position failure as an opportunity for collaborative learning and improvement.
• Recognize the nuanced differences—B2B often involves high-touch communication with tailored onboarding, while B2C relies on scalable, low-touch tools and emotional engagement.
• Establish efficient mechanisms to gather user feedback, integrate it into model updates, and communicate how their input shapes the system. This cultivates co-creation and trust.

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